Articolo in rivista, 2023, ENG, 10.1016/j.eswa.2023.120874

A text style transfer system for reducing the physician-patient expertise gap: An analysis with automatic and human evaluations

Luca Bacco, Felice Dell'Orletta, Huiyuan Lai, Mario Merone, Malvina Nissim

Università Campus Bio-Medico di Roma, Department of Engineering; Istituto di Linguistica Computazionale "Antonio Zampolli"; University of Groningen, The Netherlands; Università Campus Bio-Medico di Roma, Department of Engineering; University of Groningen, The Netherlands;

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a "Text Style Transfer" system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.

Expert systems with applications 233 , pp. 1–18

Keywords

Healthcare, Natural language processing, Text style transfer, Text simplification

CNR authors

Dell Orletta Felice

CNR institutes

ILC – Istituto di linguistica computazionale "Antonio Zampolli"

ID: 488201

Year: 2023

Type: Articolo in rivista

Creation: 2023-11-06 18:46:16.000

Last update: 2023-11-07 08:25:05.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:488201

DOI: 10.1016/j.eswa.2023.120874